HW4

1. Data Management

The data has been extracted for 2020 year to include the democratic and Republican Parties. The total votes per county by each of the winner is calculated and is shown in the PercentVotes column.We are also calculating the Winner for each of the county. We see that in 508 counties the winner party was Democrat . However Republican was winner in 2566 counties.

Create an interactive choropleth map to display the presidential election results at county level using two different colors to represent the two parties. You can choose any R library to accomplish this part of the assignment.

#library(plotly)
#library(plotly)
#library(rjson)
#library("RColorBrewer")  # brewer.pal.info for list of color scales

url <- "https://github.com/pengdsci/sta553/raw/main/data/geojson-counties-fips.json"  # contains geocode to define county boundaries in the choropleth map
counties <- rjson::fromJSON(file=url)  

# geo styling
g <- list(      scope = 'usa',
           projection = list(type = 'albers usa'),
             showland = TRUE,
 landcolor="white",
   countrywidth = 0.5,
         subunitwidth = 0.5
      
       )
###
fig <- plot_geo(df, lat = ~lat, lon = ~lon) %>% 
  add_markers( text = ~paste(county_name, winner,
                             paste("Winner:", winner), 
                             sep = "<br>"),
            color=~winner,
              symbol = "circle", 
              size = ~percentvotes , 
             colorscale = "RnBu",  
              hoverinfo = "text")   %>% 
  colorbar(title = "Parties")  %>% 
  layout( title = '2020 Presidential election Results', 
          geo = g )
          
fig

#library(plotly)
#library(rjson)
#library("RColorBrewer")  # brewer.pal.info for list of color scales

Another representation using Choropleth Map:

`summarise()` has grouped output by 'state_po'. You can override using the `.groups` argument.

fig <- plot_ly()  %>% 
  add_trace( type = "choropleth",
          geojson = counties,
        locations = df$county_fips,
                z = df$percentvotes,
           colorscale = "TealGrn",   
             zmin = 0,
             zmax = 100,
         text = ~paste("<br>County: ", df$county_name,
                           "<br>Party won: ", df$winner,
                           "<br>Winner votes percent: ", df$percentvotes,
                           "<br>Total votes: ", df$totalvotes,
                           "<br>State: ", df$state_po),

        hoverinfo = "text",
           marker = list(line=list(width=0.3))) %>% 
  colorbar(title = "Winner Votes percent(%)") %>% 
  layout( title = list(text = "<b>US Presidential elections 2020 by County</b>",
                       font = list(size = 20,
                                   color = "darkred")),
          margin = list( b = 15, l = 25, t = 85, r = 25),
          geo = g)
fig

An interesting thing to note is that this view is even more heavily dominated by the Republican. Less densely populated counties tend to vote republican, while higher density, typically smaller counties tend to vote for democrats. The result is interesting because overall the winner was Democrat Party.

Using Tableau to create the map

The link for the visualization created in Tableau public is: click here